Reconciling multiple data sources to improve accuracy of large-scale prediction of forest disease incidence

Ephraim M. Hanks, Mevin B. Hooten, Fred A. Baker

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Ecological spatial data often come from multiple sources, varying in extent and accuracy. We describe a general approach to reconciling such data sets through the use of the Bayesian hierarchical framework. This approach provides a way for the data sets to borrow strength from one another while allowing for inference on the underlying ecological process. We apply this approach to study the incidence of eastern spruce dwarf mistletoe (Arceuthobium pusillum) in Minnesota black spruce (Picea mariana). A Minnesota Department of Natural Resources operational inventory of black spruce stands in northern Minnesota found mistletoe in 11% of surveyed stands, while a small, specific-pest survey found mistletoe in 56% of the surveyed stands. We reconcile these two surveys within a Bayesian hierarchical framework and predict that 35-59% of black spruce stands in northern Minnesota are infested with dwarf mistletoe.

Original languageEnglish (US)
Pages (from-to)1173-1188
Number of pages16
JournalEcological Applications
Volume21
Issue number4
DOIs
StatePublished - Jun 2011

All Science Journal Classification (ASJC) codes

  • Ecology

Fingerprint

Dive into the research topics of 'Reconciling multiple data sources to improve accuracy of large-scale prediction of forest disease incidence'. Together they form a unique fingerprint.

Cite this